How do greenspace landscapes affect PM2.5 exposure in Wuhan? Linking spatial-nonstationary, annual varying, and multiscale perspectives
As an ambient atmospheric pollutant, fine particulate matter (PM2.5) has posed significant adverse impacts on public health around the world. To attenuate the population exposure risk to PM2.5 pollution, greenspace has been considered as a promising approach. Little is known, however, about the atte...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-07-01
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| Series: | Geo-spatial Information Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2022.2085187 |
| _version_ | 1828193696686800896 |
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| author | Qingming Zhan Chen Yang Huimin Liu |
| author_facet | Qingming Zhan Chen Yang Huimin Liu |
| author_sort | Qingming Zhan |
| collection | DOAJ |
| description | As an ambient atmospheric pollutant, fine particulate matter (PM2.5) has posed significant adverse impacts on public health around the world. To attenuate the population exposure risk to PM2.5 pollution, greenspace has been considered as a promising approach. Little is known, however, about the attenuating impacts of greenspace landscapes on PM2.5 exposure risks at various locations, scales, and exposure levels. This study employed hotspot analysis, weighted barycenter, and time-series clustering to investigate the spatiotemporal dynamics of PM2.5 exposure across Wuhan. In addition, the multi-scale geographically weighted regression (MGWR) was used to determine the relationships between greenspace landscape patterns and yearly PM2.5 exposure over four years (2000, 2005, 2010, and 2015). Results revealed that, between 2000 and 2016, the variations in PM2.5 exposure hotspot coverages within Wuhan showed an inverse U-shape trend. The K-DTW clustering differentiated the study area into seven spatial clusters with homogeneous temporal dynamics. In general, there were three stages of fluctuations in PM2.5 exposure in Wuhan: 2000–2005, 2006–2011, and 2012–2016. MGWR also disclosed associations between PM2.5 exposure and greenspace landscape parameters (AI, ED, SI, and PLAND). PLAND of green spaces can mitigate PM2.5 exposure at a broader scale (the average bandwidth was 1391), while AI, ED, and SI are generally associated with PM2.5 exposure reduction on local scales. In Wuhan, we also confirmed such relationships between four landscape metrics with varying levels of exposure risks. The results indicate that the attenuation effectiveness toward PM2.5 exposure risk by greenspace landscapes is not only site- and scale-dependent but also affected by exposure risk levels. The findings of this study can contribute to greenspace planning and management for mitigating PM2.5-attributable adverse health impacts. |
| first_indexed | 2024-04-12T09:12:59Z |
| format | Article |
| id | doaj.art-bb16394d90654eadac0d617b8a5fe24d |
| institution | Directory Open Access Journal |
| issn | 1009-5020 1993-5153 |
| language | English |
| last_indexed | 2024-04-12T09:12:59Z |
| publishDate | 2022-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geo-spatial Information Science |
| spelling | doaj.art-bb16394d90654eadac0d617b8a5fe24d2022-12-22T03:38:56ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532022-07-0111610.1080/10095020.2022.2085187How do greenspace landscapes affect PM2.5 exposure in Wuhan? Linking spatial-nonstationary, annual varying, and multiscale perspectivesQingming Zhan0Chen Yang1Huimin Liu2School of Urban Design, Wuhan University, Wuhan, ChinaCollege of Urban and Environmental Sciences, Peking University, Beijing, ChinaSchool of Urban Design, Wuhan University, Wuhan, ChinaAs an ambient atmospheric pollutant, fine particulate matter (PM2.5) has posed significant adverse impacts on public health around the world. To attenuate the population exposure risk to PM2.5 pollution, greenspace has been considered as a promising approach. Little is known, however, about the attenuating impacts of greenspace landscapes on PM2.5 exposure risks at various locations, scales, and exposure levels. This study employed hotspot analysis, weighted barycenter, and time-series clustering to investigate the spatiotemporal dynamics of PM2.5 exposure across Wuhan. In addition, the multi-scale geographically weighted regression (MGWR) was used to determine the relationships between greenspace landscape patterns and yearly PM2.5 exposure over four years (2000, 2005, 2010, and 2015). Results revealed that, between 2000 and 2016, the variations in PM2.5 exposure hotspot coverages within Wuhan showed an inverse U-shape trend. The K-DTW clustering differentiated the study area into seven spatial clusters with homogeneous temporal dynamics. In general, there were three stages of fluctuations in PM2.5 exposure in Wuhan: 2000–2005, 2006–2011, and 2012–2016. MGWR also disclosed associations between PM2.5 exposure and greenspace landscape parameters (AI, ED, SI, and PLAND). PLAND of green spaces can mitigate PM2.5 exposure at a broader scale (the average bandwidth was 1391), while AI, ED, and SI are generally associated with PM2.5 exposure reduction on local scales. In Wuhan, we also confirmed such relationships between four landscape metrics with varying levels of exposure risks. The results indicate that the attenuation effectiveness toward PM2.5 exposure risk by greenspace landscapes is not only site- and scale-dependent but also affected by exposure risk levels. The findings of this study can contribute to greenspace planning and management for mitigating PM2.5-attributable adverse health impacts.https://www.tandfonline.com/doi/10.1080/10095020.2022.2085187PM2.5 exposuregreenspacespatiotemporal variationsmulti-scale geographically weighted regressiontime-series clustering |
| spellingShingle | Qingming Zhan Chen Yang Huimin Liu How do greenspace landscapes affect PM2.5 exposure in Wuhan? Linking spatial-nonstationary, annual varying, and multiscale perspectives Geo-spatial Information Science PM2.5 exposure greenspace spatiotemporal variations multi-scale geographically weighted regression time-series clustering |
| title | How do greenspace landscapes affect PM2.5 exposure in Wuhan? Linking spatial-nonstationary, annual varying, and multiscale perspectives |
| title_full | How do greenspace landscapes affect PM2.5 exposure in Wuhan? Linking spatial-nonstationary, annual varying, and multiscale perspectives |
| title_fullStr | How do greenspace landscapes affect PM2.5 exposure in Wuhan? Linking spatial-nonstationary, annual varying, and multiscale perspectives |
| title_full_unstemmed | How do greenspace landscapes affect PM2.5 exposure in Wuhan? Linking spatial-nonstationary, annual varying, and multiscale perspectives |
| title_short | How do greenspace landscapes affect PM2.5 exposure in Wuhan? Linking spatial-nonstationary, annual varying, and multiscale perspectives |
| title_sort | how do greenspace landscapes affect pm2 5 exposure in wuhan linking spatial nonstationary annual varying and multiscale perspectives |
| topic | PM2.5 exposure greenspace spatiotemporal variations multi-scale geographically weighted regression time-series clustering |
| url | https://www.tandfonline.com/doi/10.1080/10095020.2022.2085187 |
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